1. Field of the Invention
The present invention is directed generally to sequence detectors, and, more particularly, to sequence detectors in ISI memory channels.
2. Description of the Background
In recent years, there has been a major shift in the design of signal detectors in magnetic recording. Traditional peak detectors (PD), such as those described in Nakagawa et al., xe2x80x9cA Study of Detection Methods of NRZ Recordingxe2x80x9d, IEEE Trans. Magn., vol. 16, pp. 1041-110, January 1980, have been replaced by Viterbi-like detectors in the form of partial response maximum likelihood (PRML) schemes or hybrids between tree/trellis detectors and decision feedback equalizers (DFE), such as FDTS/DF, MDFE and RAM-RSE. These methods were derived under the assumption that additive white Gausian noise (AWGN) is present in the system. The resulting trellis/tree branch metrics are then computed as Euclidian distances.
It has long been observed that the noise in magnetic recording systems is neither white nor stationary. The nonstationarity of the media noise results from its signal dependent nature. Combating media noise and its signal dependence has thus far been confined to modifying the Euclidian branch metric to account for these effects. Zeng, et al., xe2x80x9cModified Viterbi Algorithm for Jitter-Dominated 1-D2 Channel,xe2x80x9d IEEE Trans. Magn., Vol. MAG-28, pp. 2895-97, September, 1992, and Lee et al., xe2x80x9cPerformance Analysis of the Modified maximum Likelihood Sequence Detector in the Presence of Data-Dependent Noise,xe2x80x9d Proceedings 26th Asilomar Conference, pp. 961-64, October 1992 have derived a branch metric computation method for combating the signal-dependent character of media noise. These references ignore the correlation between noise samples. The effectiveness of this method has been demonstrated on real data in Zayad et al., xe2x80x9cComparison of Equalization and Detection for Very High-Density Magnetic Recording,xe2x80x9d IEEE INTERMAG Conference, New Orleans, April 1997.
These methods do not take into consideration the correlation between noise samples in the readback signal. These correlations arise due to noise coloring by front-end equalizers, media noise, media nonlinearities, and magnetoresistive (MR) head nonlinearities. This noise coloring causes significant performance degradation at high recording densities. Thus, there is a need for an adaptive correlation-sensitive maximum likelihood sequence detector which derives the maximum likelihood sequence detector (MLSD) without making the usual simplifying assumption that the noise samples are independent random variables.
Turbo codes were introduced in 1993 and hold the promise of substantial coding gains over current coding algorithms, and their performance is within a fraction of a dB of the Shannon theoretical limit for additive white Gaussian noise channels. The basic idea in turbo decoding and other iterative decoding strategies is to pass xe2x80x9csoftxe2x80x9d information between several components of the decoder and the detector. In this context, the detector is the first device that processes data which is observed at the output of the communications channel. Classically, the detector is a hard-detection device which provides zeroes and ones at its output. A Viterbi detector is a typical example of such a hard detector. When iterative decoding is used, however, the detector is often a soft detector in which the outputs of the detector are reliability measures for bits transmitted through the communications channel. Because the detector is the first device that processes the channel output, the detector should be tuned to the channel signal and noise statistics. However, existing soft output detectors are designed only for channels which are assumed to have white noise. Thus, there is a need for a soft detector which is designed for channels which have correlated and/or signal-dependent noise.
The present invention is directed to a method of determining branch metric values in a detector. The method includes receiving a plurality of time variant signal samples, the signal samples having one of signal-dependent noise, correlated noise, and both signal dependent and correlated noise associated therewith. The method also includes selecting a branch metric function at a certain time index and applying the selected function to the signal samples to determine the metric values.
The present invention represents a substantial advance over prior sequence detectors. Because the present invention takes into account the correlation between noise samples in the readback signal, the detected data sequence is detected with a higher degree of accuracy. Those advantages and benefits of the present invention, and others, will become apparent from the Detailed Description of the Invention hereinbelow.